1. Field of the Invention
The present invention relates to pattern recognition, and more particularly to a pattern recognition device for recognizing a pattern by estimating a category to which an input pattern or its feature vector belongs, and a method thereof.
2. Related Art of the Invention
Recently, a system for electronically filing a document, and coding the document depending on need, has been developed in order to make work flow in a company more efficient. Accordingly, there is a strong need for a document recognition device which recognizes low quality documents, such as a faxed document, etc. Especially, a character recognition device is essential for coding printed character string information. For its practical and widespread use, it is vital to estimate a character category at a higher speed while maintaining high recognition accuracy.
Additionally, technology for recognizing a human face becomes more important as a technical element of electronic conferencing and a security system. Human face recognition technology, which allows a human face to be identified in real time at high speed and with high accuracy, is demanded.
As systems such as a computer graphics system, CAD (Computer-Aided Design) system, DTP (Desk Top Publishing) system etc., become popular, a technique for recognizing a three-dimensional object or a two-dimensional graphic object becomes more important as the means for efficiently inputting an actual three-dimensional or two-dimensional object to a computer, and thereby implementing its reuse. Accordingly, the technology for recognizing a real object or a graphic object at high speed and with high accuracy is practically essential for these systems.
As described above, the technology for recognizing a pattern at high speed and with high accuracy plays an important role as a technical element in configuring a variety of types of practical pattern recognition devices. Here, the terms which are frequently used for pattern recognition are briefly defined below.
A recognition target is referred to as a pattern, and a set formed by all patterns is referred to as a pattern space. A combination of one or more feature amounts obtained by extracting a feature from a pattern is referred to as a feature vector. The number of elements of the feature vector is referred to as the dimension of the feature vector.
A combination of the values of elements of a feature vector is referred to as the value of the feature vector. A set formed by all the values of the feature vector is referred to as a feature space. The dimension of the feature space is equal to that of the feature vector, which is the element of the feature space.
A subset of the elements of the feature vector is referred to as a partial feature vector. A combination of the values of respective elements of the partial feature vector is referred to as the value of the partial feature vector. A set formed by all the values of the partial feature vector is referred to as a partial feature space. The dimension of the partial feature space is equal to that of the partial feature vector, which is the element of the partial feature space.
A set of patterns or feature vectors which can be recognized as being an identical type is referred to as a category. Especially, a set of patterns which can be recognized as being an identical type is referred to as a category pattern set, while a set of feature vectors which can be recognized as being an identical type is referred to as a category feature set.
To determine which category (category pattern set or category feature set) an input pattern or feature vector belongs to is referred to as pattern recognition. Especially, if there is a probability that the input pattern or feature vector belongs to a certain category included in a category set, that category set is referred to as a candidate category set.
There is a conventional method for significantly reducing a process time by compressing a feature, and drastically reducing the number of dimensions of a feature vector used for calculating a distance when matching is made, as a fast pattern recognition method. FIG. 1 is a block diagram showing the configuration of such a pattern recognition device employing high-speed classification by compressing a feature.
In the pattern recognition device shown in FIG. 1, a feature extracting unit 1 extracts a feature vector from an input pattern; a feature compressing unit 2 performs linear transformation of the feature vector, and obtains a compressed feature vector whose number of dimensions becomes lower; a compressed feature dictionary 4 includes compressed feature vectors corresponding to respective categories; and a rough classification performing unit 3 obtains the distance between a compressed feature vector obtained by the feature compressing unit 2 and each compressed feature vector in the compressed feature dictionary 4, sorts categories in ascending order of distance, and outputs categories whose number is predetermined, starting from a category whose distance is the shortest, as a candidate category set.
However, the conventional pattern recognition has the following problems.
With pattern recognition using high speed classification by compressing a feature, a loss of information occurs when a feature vector is compressed and transformed into a compressed feature vector whose number of dimensions is lower. Accordingly, a correct candidate category set may not sometimes be obtained. This is because the category, including a compressed feature vector whose distance to a compressed feature vector of the input pattern is the shortest, does not always contain the compressed feature vector of the input pattern. Consequently, the recognition accuracy of an input pattern of low quality is significantly degraded. Also the recognition accuracy of an input pattern of high quality is slightly degraded.
An object of the present invention is to provide a pattern recognition device which can make the calculation of a candidate category set faster while suppressing the degradation of a recognition accuracy, and a method thereof.
As a first aspect of the present invention, a pattern recognition device comprises a table storing unit and a candidate category calculating unit. The table storing unit stores a candidate table describing the information required for generating a mapping whose input is a value of a reference feature vector calculated from a feature vector of a pattern, and whose output is a candidate category set. The candidate category calculating unit obtains a candidate category set corresponding to a given value of a reference feature vector using the candidate table, and outputs the obtained candidate category set.
The reference feature vector is a feature vector referenced by the candidate category calculating unit. For example, a partial feature vector composed of part of the elements of the feature vector is used as the reference feature vector.
As a second aspect of the present invention, a pattern recognition device comprises a plurality of table storing units, a plurality of candidate category calculating units, and a category screening unit. Each of the plurality of table storing units stores a candidate table describing the information required for generating a mapping whose input is a value of a reference feature vector calculated from a feature vector of a pattern, and whose output is a candidate category set.
The candidate category calculating unit is arranged for each of the plurality of table storing units, obtains a candidate category set corresponding to a value of a given reference feature vector using a candidate table, and outputs the obtained candidate category set. The category screening unit screens a plurality of candidate category sets output from a plurality of candidate category calculating units and outputs a result of the screening.
As a third aspect of the present invention, a pattern recognition device comprises a storing unit and a candidate category calculating unit. The storing unit stores a correspondence between feature amount data indicating a feature of a pattern and a candidate category set. The candidate category calculating unit obtains a candidate category set corresponding to given feature amount data by using the correspondence, and outputs the obtained candidate category set.